Aligning Word2vec word embeddings using a compass in a system of Compass-aligned Distributional Embeddings (CADE) creates stable and accurate temporal word embeddings. This thesis seeks to expand the CADE framework into the area of dynamic topic modeling (DTM), where temporal word2vec embeddings can be used to describe temporally and unsupervised evolving topics. It also seeks to improve upon the CADE framework through a theoretical and experimental exploration of compass parameters, cluster and topic generation techniques, and topic descriptor creation. This method of Temporal Topic Embeddings with a Compass (TTEC) will be compared to other DTM techniques in the ability to create coherent and diverse clusters and will be shown to be competitive compared to traditional and transformer-aided DTM architectures. In addition to a qualitative discussion of results, there will be a political theoretical overview of the nature of this technique and potential use cases, with interviews from political actors of various backgrounds as to how the technique and machine learning as a whole can be used in the organizational setting. / Master of Science / Diachronic word embeddings look at how the context words appear in evolve over time. Dynamic Topic Modeling (DTM) is the ability to computationally discover topics and how they evolve over time. This thesis creates a DTM technique called Temporal Topic Embeddings with a Compass (TTEC) based off diachronic word embeddings, allowing a user to simultaneously look at word and topic evolution over time. There is also an exploration of the use case of TTEC and similar machine learning models within various political organizational settings through interviews.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/119057 |
| Date | 22 May 2024 |
| Creators | Palamarchuk, Daniel Andrew |
| Contributors | Computer Science and#38; Applications, North, Christopher L., Danielson, Thomas Lee, Mayer, Brian Benjamin, Wang, Xuan |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International, http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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